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General

Evaluating LLM Safety

Ensuring model outputs are safe is essential for production systems. This post outlines lightweight tests and metrics to evaluate safety properties.

Key dimensions

  • Toxicity: Use automated classifiers and human review to detect offensive language.
  • Privacy leakage: Test for extraction of sensitive or training-data-specific content.
  • Instruction adherence: Confirm the model refuses or safely responds to disallowed instructions.

Testing approach

  1. Create adversarial prompt suites (edge cases, illicit instructions).
  2. Run automated detectors and filter obvious violations.
  3. Sample flagged outputs for human review and action.

Mitigations

  • Prompt-level: add safety guardrails, explicit refusals, and role constraints.
  • System-level: apply filters, moderation layers, and rate limits.

Regularly update tests and datasets as new risks surface.